Overview

Dataset statistics

Number of variables60
Number of observations202599
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory94.3 MiB
Average record size in memory488.0 B

Variable types

Text1
Categorical41
Numeric18

Alerts

x_1_x is highly overall correlated with x_1_yHigh correlation
y_1_x is highly overall correlated with y_1_yHigh correlation
width_x is highly overall correlated with height_x and 2 other fieldsHigh correlation
height_x is highly overall correlated with width_x and 2 other fieldsHigh correlation
x_1_y is highly overall correlated with x_1_xHigh correlation
y_1_y is highly overall correlated with y_1_xHigh correlation
width_y is highly overall correlated with width_x and 2 other fieldsHigh correlation
height_y is highly overall correlated with width_x and 2 other fieldsHigh correlation
lefteye_x is highly overall correlated with righteye_x and 5 other fieldsHigh correlation
lefteye_y is highly overall correlated with righteye_x and 2 other fieldsHigh correlation
righteye_x is highly overall correlated with lefteye_x and 5 other fieldsHigh correlation
righteye_y is highly overall correlated with lefteye_x and 2 other fieldsHigh correlation
leftmouth_x is highly overall correlated with lefteye_x and 7 other fieldsHigh correlation
leftmouth_y is highly overall correlated with lefteye_x and 4 other fieldsHigh correlation
rightmouth_x is highly overall correlated with lefteye_x and 6 other fieldsHigh correlation
rightmouth_y is highly overall correlated with lefteye_x and 3 other fieldsHigh correlation
5_o_Clock_Shadow is highly overall correlated with No_BeardHigh correlation
Chubby is highly overall correlated with Double_ChinHigh correlation
Double_Chin is highly overall correlated with ChubbyHigh correlation
Goatee is highly overall correlated with No_Beard and 1 other fieldsHigh correlation
Heavy_Makeup is highly overall correlated with Male and 1 other fieldsHigh correlation
High_Cheekbones is highly overall correlated with leftmouth_x and 2 other fieldsHigh correlation
Male is highly overall correlated with Heavy_Makeup and 2 other fieldsHigh correlation
Mouth_Slightly_Open is highly overall correlated with SmilingHigh correlation
No_Beard is highly overall correlated with 5_o_Clock_Shadow and 3 other fieldsHigh correlation
Sideburns is highly overall correlated with Goatee and 1 other fieldsHigh correlation
Smiling is highly overall correlated with leftmouth_x and 3 other fieldsHigh correlation
Wearing_Lipstick is highly overall correlated with Heavy_Makeup and 1 other fieldsHigh correlation
Bald is highly imbalanced (84.5%)Imbalance
Blurry is highly imbalanced (71.0%)Imbalance
Chubby is highly imbalanced (68.2%)Imbalance
Double_Chin is highly imbalanced (72.8%)Imbalance
Eyeglasses is highly imbalanced (65.3%)Imbalance
Goatee is highly imbalanced (66.2%)Imbalance
Gray_Hair is highly imbalanced (74.9%)Imbalance
Mustache is highly imbalanced (75.1%)Imbalance
Pale_Skin is highly imbalanced (74.4%)Imbalance
Receding_Hairline is highly imbalanced (59.9%)Imbalance
Rosy_Cheeks is highly imbalanced (65.0%)Imbalance
Sideburns is highly imbalanced (68.7%)Imbalance
Wearing_Hat is highly imbalanced (72.0%)Imbalance
Wearing_Necktie is highly imbalanced (62.4%)Imbalance
image_id has unique valuesUnique

Reproduction

Analysis started2023-06-17 09:44:04.933832
Analysis finished2023-06-17 09:45:50.636649
Duration1 minute and 45.7 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Distinct202599
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2023-06-17T11:45:50.972208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2025990
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202599 ?
Unique (%)100.0%

Sample

1st row000001.jpg
2nd row000002.jpg
3rd row000003.jpg
4th row000004.jpg
5th row000005.jpg
ValueCountFrequency (%)
000001.jpg 1
 
< 0.1%
000025.jpg 1
 
< 0.1%
000024.jpg 1
 
< 0.1%
000003.jpg 1
 
< 0.1%
000004.jpg 1
 
< 0.1%
000005.jpg 1
 
< 0.1%
000006.jpg 1
 
< 0.1%
000007.jpg 1
 
< 0.1%
000008.jpg 1
 
< 0.1%
000009.jpg 1
 
< 0.1%
Other values (202589) 202589
> 99.9%
2023-06-17T11:45:51.333258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 204414
10.1%
. 202599
10.0%
j 202599
10.0%
p 202599
10.0%
g 202599
10.0%
1 201820
10.0%
2 104020
 
5.1%
3 100820
 
5.0%
5 100820
 
5.0%
4 100820
 
5.0%
Other values (4) 402880
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1215594
60.0%
Lowercase Letter 607797
30.0%
Other Punctuation 202599
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 204414
16.8%
1 201820
16.6%
2 104020
8.6%
3 100820
8.3%
5 100820
8.3%
4 100820
8.3%
6 100720
8.3%
7 100720
8.3%
8 100720
8.3%
9 100720
8.3%
Lowercase Letter
ValueCountFrequency (%)
j 202599
33.3%
p 202599
33.3%
g 202599
33.3%
Other Punctuation
ValueCountFrequency (%)
. 202599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1418193
70.0%
Latin 607797
30.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 204414
14.4%
. 202599
14.3%
1 201820
14.2%
2 104020
7.3%
3 100820
7.1%
5 100820
7.1%
4 100820
7.1%
6 100720
7.1%
7 100720
7.1%
8 100720
7.1%
Latin
ValueCountFrequency (%)
j 202599
33.3%
p 202599
33.3%
g 202599
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2025990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 204414
10.1%
. 202599
10.0%
j 202599
10.0%
p 202599
10.0%
g 202599
10.0%
1 201820
10.0%
2 104020
 
5.1%
3 100820
 
5.0%
5 100820
 
5.0%
4 100820
 
5.0%
Other values (4) 402880
19.9%

5_o_Clock_Shadow
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
180083 
1
22516 

Length

Max length2
Median length2
Mean length1.8888642
Min length1

Characters and Unicode

Total characters382682
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 180083
88.9%
1 22516
 
11.1%

Length

2023-06-17T11:45:51.440344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:51.544972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
52.9%
- 180083
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
52.9%
Dash Punctuation 180083
47.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 180083
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 382682
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
52.9%
- 180083
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 382682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
52.9%
- 180083
47.1%

Arched_Eyebrows
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
148509 
1
54090 

Length

Max length2
Median length2
Mean length1.7330194
Min length1

Characters and Unicode

Total characters351108
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row-1
4th row-1
5th row1

Common Values

ValueCountFrequency (%)
-1 148509
73.3%
1 54090
 
26.7%

Length

2023-06-17T11:45:51.626446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:51.743214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
57.7%
- 148509
42.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
57.7%
Dash Punctuation 148509
42.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 148509
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 351108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
57.7%
- 148509
42.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
57.7%
- 148509
42.3%

Attractive
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
1
103833 
-1
98766 

Length

Max length2
Median length1
Mean length1.487495
Min length1

Characters and Unicode

Total characters301365
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row-1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 103833
51.3%
-1 98766
48.7%

Length

2023-06-17T11:45:51.827904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:51.920133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
67.2%
- 98766
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
67.2%
Dash Punctuation 98766
32.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 98766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301365
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
67.2%
- 98766
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301365
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
67.2%
- 98766
32.8%

Bags_Under_Eyes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
161153 
1
41446 

Length

Max length2
Median length2
Mean length1.7954284
Min length1

Characters and Unicode

Total characters363752
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 161153
79.5%
1 41446
 
20.5%

Length

2023-06-17T11:45:51.996843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:52.092707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
55.7%
- 161153
44.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
55.7%
Dash Punctuation 161153
44.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 161153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363752
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
55.7%
- 161153
44.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
55.7%
- 161153
44.3%

Bald
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
198052 
1
 
4547

Length

Max length2
Median length2
Mean length1.9775567
Min length1

Characters and Unicode

Total characters400651
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 198052
97.8%
1 4547
 
2.2%

Length

2023-06-17T11:45:52.176259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:52.271952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
50.6%
- 198052
49.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
50.6%
Dash Punctuation 198052
49.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 198052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 400651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
50.6%
- 198052
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
50.6%
- 198052
49.4%

Bangs
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
171890 
1
30709 

Length

Max length2
Median length2
Mean length1.8484247
Min length1

Characters and Unicode

Total characters374489
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 171890
84.8%
1 30709
 
15.2%

Length

2023-06-17T11:45:52.353669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:52.448182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
54.1%
- 171890
45.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
54.1%
Dash Punctuation 171890
45.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 171890
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 374489
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
54.1%
- 171890
45.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 374489
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
54.1%
- 171890
45.9%

Big_Lips
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
153814 
1
48785 

Length

Max length2
Median length2
Mean length1.7592041
Min length1

Characters and Unicode

Total characters356413
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row1
4th row-1
5th row1

Common Values

ValueCountFrequency (%)
-1 153814
75.9%
1 48785
 
24.1%

Length

2023-06-17T11:45:52.529789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:52.624330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
56.8%
- 153814
43.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
56.8%
Dash Punctuation 153814
43.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 153814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 356413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
56.8%
- 153814
43.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 356413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
56.8%
- 153814
43.2%

Big_Nose
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
155083 
1
47516 

Length

Max length2
Median length2
Mean length1.7654677
Min length1

Characters and Unicode

Total characters357682
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 155083
76.5%
1 47516
 
23.5%

Length

2023-06-17T11:45:52.709047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:52.804281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
56.6%
- 155083
43.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
56.6%
Dash Punctuation 155083
43.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 155083
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 357682
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
56.6%
- 155083
43.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 357682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
56.6%
- 155083
43.4%

Black_Hair
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
154127 
1
48472 

Length

Max length2
Median length2
Mean length1.7607491
Min length1

Characters and Unicode

Total characters356726
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 154127
76.1%
1 48472
 
23.9%

Length

2023-06-17T11:45:52.887621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:52.984102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
56.8%
- 154127
43.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
56.8%
Dash Punctuation 154127
43.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 154127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 356726
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
56.8%
- 154127
43.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 356726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
56.8%
- 154127
43.2%

Blond_Hair
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
172616 
1
29983 

Length

Max length2
Median length2
Mean length1.8520082
Min length1

Characters and Unicode

Total characters375215
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 172616
85.2%
1 29983
 
14.8%

Length

2023-06-17T11:45:53.063701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:53.159198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
54.0%
- 172616
46.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
54.0%
Dash Punctuation 172616
46.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 172616
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 375215
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
54.0%
- 172616
46.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 375215
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
54.0%
- 172616
46.0%

Blurry
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
192287 
1
 
10312

Length

Max length2
Median length2
Mean length1.9491014
Min length1

Characters and Unicode

Total characters394886
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 192287
94.9%
1 10312
 
5.1%

Length

2023-06-17T11:45:53.237581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:53.325547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
51.3%
- 192287
48.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
51.3%
Dash Punctuation 192287
48.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 192287
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 394886
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
51.3%
- 192287
48.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 394886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
51.3%
- 192287
48.7%

Brown_Hair
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
161027 
1
41572 

Length

Max length2
Median length2
Mean length1.7948065
Min length1

Characters and Unicode

Total characters363626
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 161027
79.5%
1 41572
 
20.5%

Length

2023-06-17T11:45:53.401687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:53.491572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
55.7%
- 161027
44.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
55.7%
Dash Punctuation 161027
44.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 161027
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363626
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
55.7%
- 161027
44.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
55.7%
- 161027
44.3%

Bushy_Eyebrows
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
173796 
1
28803 

Length

Max length2
Median length2
Mean length1.8578325
Min length1

Characters and Unicode

Total characters376395
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 173796
85.8%
1 28803
 
14.2%

Length

2023-06-17T11:45:53.569217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:53.661340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
53.8%
- 173796
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
53.8%
Dash Punctuation 173796
46.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 173796
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 376395
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
53.8%
- 173796
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
53.8%
- 173796
46.2%

Chubby
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
190936 
1
 
11663

Length

Max length2
Median length2
Mean length1.9424331
Min length1

Characters and Unicode

Total characters393535
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 190936
94.2%
1 11663
 
5.8%

Length

2023-06-17T11:45:53.736926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:53.825089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
51.5%
- 190936
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
51.5%
Dash Punctuation 190936
48.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 190936
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 393535
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
51.5%
- 190936
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 393535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
51.5%
- 190936
48.5%

Double_Chin
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
193140 
1
 
9459

Length

Max length2
Median length2
Mean length1.9533117
Min length1

Characters and Unicode

Total characters395739
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 193140
95.3%
1 9459
 
4.7%

Length

2023-06-17T11:45:53.899952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:53.988411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
51.2%
- 193140
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
51.2%
Dash Punctuation 193140
48.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 193140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 395739
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
51.2%
- 193140
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 395739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
51.2%
- 193140
48.8%

Eyeglasses
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
189406 
1
 
13193

Length

Max length2
Median length2
Mean length1.9348812
Min length1

Characters and Unicode

Total characters392005
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 189406
93.5%
1 13193
 
6.5%

Length

2023-06-17T11:45:54.064935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:54.157520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
51.7%
- 189406
48.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
51.7%
Dash Punctuation 189406
48.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 189406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 392005
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
51.7%
- 189406
48.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 392005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
51.7%
- 189406
48.3%

Goatee
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
189883 
1
 
12716

Length

Max length2
Median length2
Mean length1.9372356
Min length1

Characters and Unicode

Total characters392482
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 189883
93.7%
1 12716
 
6.3%

Length

2023-06-17T11:45:54.233137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:54.325508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
51.6%
- 189883
48.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
51.6%
Dash Punctuation 189883
48.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 189883
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 392482
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
51.6%
- 189883
48.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 392482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
51.6%
- 189883
48.4%

Gray_Hair
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
194100 
1
 
8499

Length

Max length2
Median length2
Mean length1.9580501
Min length1

Characters and Unicode

Total characters396699
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 194100
95.8%
1 8499
 
4.2%

Length

2023-06-17T11:45:54.401770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:54.509438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
51.1%
- 194100
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
51.1%
Dash Punctuation 194100
48.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 194100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 396699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
51.1%
- 194100
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
51.1%
- 194100
48.9%

Heavy_Makeup
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
124209 
1
78390 

Length

Max length2
Median length2
Mean length1.6130781
Min length1

Characters and Unicode

Total characters326808
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row-1
4th row-1
5th row1

Common Values

ValueCountFrequency (%)
-1 124209
61.3%
1 78390
38.7%

Length

2023-06-17T11:45:54.613681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:54.737590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
62.0%
- 124209
38.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
62.0%
Dash Punctuation 124209
38.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 124209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 326808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
62.0%
- 124209
38.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 326808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
62.0%
- 124209
38.0%

High_Cheekbones
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
110410 
1
92189 

Length

Max length2
Median length2
Mean length1.5449681
Min length1

Characters and Unicode

Total characters313009
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 110410
54.5%
1 92189
45.5%

Length

2023-06-17T11:45:54.836191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:54.934555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
64.7%
- 110410
35.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
64.7%
Dash Punctuation 110410
35.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 110410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 313009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
64.7%
- 110410
35.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 313009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
64.7%
- 110410
35.3%

Male
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
118165 
1
84434 

Length

Max length2
Median length2
Mean length1.5832457
Min length1

Characters and Unicode

Total characters320764
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 118165
58.3%
1 84434
41.7%

Length

2023-06-17T11:45:55.022927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:55.128630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
63.2%
- 118165
36.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
63.2%
Dash Punctuation 118165
36.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 118165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 320764
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
63.2%
- 118165
36.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 320764
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
63.2%
- 118165
36.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
104657 
1
97942 

Length

Max length2
Median length2
Mean length1.5165721
Min length1

Characters and Unicode

Total characters307256
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 104657
51.7%
1 97942
48.3%

Length

2023-06-17T11:45:55.213692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:55.311865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
65.9%
- 104657
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
65.9%
Dash Punctuation 104657
34.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 104657
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 307256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
65.9%
- 104657
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 307256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
65.9%
- 104657
34.1%

Mustache
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
194182 
1
 
8417

Length

Max length2
Median length2
Mean length1.9584549
Min length1

Characters and Unicode

Total characters396781
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 194182
95.8%
1 8417
 
4.2%

Length

2023-06-17T11:45:55.392139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:55.483562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
51.1%
- 194182
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
51.1%
Dash Punctuation 194182
48.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 194182
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 396781
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
51.1%
- 194182
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
51.1%
- 194182
48.9%

Narrow_Eyes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
179270 
1
23329 

Length

Max length2
Median length2
Mean length1.8848514
Min length1

Characters and Unicode

Total characters381869
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row1
4th row-1
5th row1

Common Values

ValueCountFrequency (%)
-1 179270
88.5%
1 23329
 
11.5%

Length

2023-06-17T11:45:55.564265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:55.664557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
53.1%
- 179270
46.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
53.1%
Dash Punctuation 179270
46.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 179270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 381869
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
53.1%
- 179270
46.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 381869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
53.1%
- 179270
46.9%

No_Beard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
1
169158 
-1
33441 

Length

Max length2
Median length1
Mean length1.16506
Min length1

Characters and Unicode

Total characters236040
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 169158
83.5%
-1 33441
 
16.5%

Length

2023-06-17T11:45:55.763716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:55.877238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
85.8%
- 33441
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
85.8%
Dash Punctuation 33441
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33441
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 236040
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
85.8%
- 33441
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 236040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
85.8%
- 33441
 
14.2%

Oval_Face
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
145032 
1
57567 

Length

Max length2
Median length2
Mean length1.7158574
Min length1

Characters and Unicode

Total characters347631
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 145032
71.6%
1 57567
 
28.4%

Length

2023-06-17T11:45:55.968434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:56.066272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
58.3%
- 145032
41.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
58.3%
Dash Punctuation 145032
41.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 145032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 347631
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
58.3%
- 145032
41.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 347631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
58.3%
- 145032
41.7%

Pale_Skin
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
193898 
1
 
8701

Length

Max length2
Median length2
Mean length1.9570531
Min length1

Characters and Unicode

Total characters396497
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 193898
95.7%
1 8701
 
4.3%

Length

2023-06-17T11:45:56.144755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:56.235382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
51.1%
- 193898
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
51.1%
Dash Punctuation 193898
48.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 193898
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 396497
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
51.1%
- 193898
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396497
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
51.1%
- 193898
48.9%

Pointy_Nose
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
146389 
1
56210 

Length

Max length2
Median length2
Mean length1.7225554
Min length1

Characters and Unicode

Total characters348988
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
-1 146389
72.3%
1 56210
 
27.7%

Length

2023-06-17T11:45:56.314159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:56.408479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
58.1%
- 146389
41.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
58.1%
Dash Punctuation 146389
41.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 146389
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 348988
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
58.1%
- 146389
41.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 348988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
58.1%
- 146389
41.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
186436 
1
 
16163

Length

Max length2
Median length2
Mean length1.9202217
Min length1

Characters and Unicode

Total characters389035
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 186436
92.0%
1 16163
 
8.0%

Length

2023-06-17T11:45:56.488075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:56.582977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
52.1%
- 186436
47.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
52.1%
Dash Punctuation 186436
47.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 186436
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 389035
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
52.1%
- 186436
47.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 389035
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
52.1%
- 186436
47.9%

Rosy_Cheeks
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
189284 
1
 
13315

Length

Max length2
Median length2
Mean length1.934279
Min length1

Characters and Unicode

Total characters391883
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 189284
93.4%
1 13315
 
6.6%

Length

2023-06-17T11:45:56.660891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:56.757513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
51.7%
- 189284
48.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
51.7%
Dash Punctuation 189284
48.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 189284
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 391883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
51.7%
- 189284
48.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 391883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
51.7%
- 189284
48.3%

Sideburns
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
191150 
1
 
11449

Length

Max length2
Median length2
Mean length1.9434894
Min length1

Characters and Unicode

Total characters393749
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 191150
94.3%
1 11449
 
5.7%

Length

2023-06-17T11:45:56.834183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:56.924052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
51.5%
- 191150
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
51.5%
Dash Punctuation 191150
48.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 191150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 393749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
51.5%
- 191150
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 393749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
51.5%
- 191150
48.5%

Smiling
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
104930 
1
97669 

Length

Max length2
Median length2
Mean length1.5179196
Min length1

Characters and Unicode

Total characters307529
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 104930
51.8%
1 97669
48.2%

Length

2023-06-17T11:45:57.000475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:57.087244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
65.9%
- 104930
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
65.9%
Dash Punctuation 104930
34.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 104930
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 307529
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
65.9%
- 104930
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 307529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
65.9%
- 104930
34.1%

Straight_Hair
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
160377 
1
42222 

Length

Max length2
Median length2
Mean length1.7915982
Min length1

Characters and Unicode

Total characters362976
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row-1
4th row1
5th row-1

Common Values

ValueCountFrequency (%)
-1 160377
79.2%
1 42222
 
20.8%

Length

2023-06-17T11:45:57.166667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:57.263400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
55.8%
- 160377
44.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
55.8%
Dash Punctuation 160377
44.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 160377
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 362976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
55.8%
- 160377
44.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 362976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
55.8%
- 160377
44.2%

Wavy_Hair
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
137855 
1
64744 

Length

Max length2
Median length2
Mean length1.6804328
Min length1

Characters and Unicode

Total characters340454
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 137855
68.0%
1 64744
32.0%

Length

2023-06-17T11:45:57.341923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:57.434131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
59.5%
- 137855
40.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
59.5%
Dash Punctuation 137855
40.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 137855
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 340454
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
59.5%
- 137855
40.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340454
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
59.5%
- 137855
40.5%

Wearing_Earrings
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
164323 
1
38276 

Length

Max length2
Median length2
Mean length1.8110751
Min length1

Characters and Unicode

Total characters366922
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row-1
4th row1
5th row-1

Common Values

ValueCountFrequency (%)
-1 164323
81.1%
1 38276
 
18.9%

Length

2023-06-17T11:45:57.509055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:57.609980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
55.2%
- 164323
44.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
55.2%
Dash Punctuation 164323
44.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 164323
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 366922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
55.2%
- 164323
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 366922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
55.2%
- 164323
44.8%

Wearing_Hat
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
192781 
1
 
9818

Length

Max length2
Median length2
Mean length1.9515397
Min length1

Characters and Unicode

Total characters395380
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 192781
95.2%
1 9818
 
4.8%

Length

2023-06-17T11:45:57.690775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:57.779406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
51.2%
- 192781
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
51.2%
Dash Punctuation 192781
48.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 192781
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 395380
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
51.2%
- 192781
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 395380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
51.2%
- 192781
48.8%

Wearing_Lipstick
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
106884 
1
95715 

Length

Max length2
Median length2
Mean length1.5275643
Min length1

Characters and Unicode

Total characters309483
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row-1
3rd row-1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
-1 106884
52.8%
1 95715
47.2%

Length

2023-06-17T11:45:57.855838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:57.946812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
65.5%
- 106884
34.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
65.5%
Dash Punctuation 106884
34.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 106884
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 309483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
65.5%
- 106884
34.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 309483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
65.5%
- 106884
34.5%

Wearing_Necklace
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
177686 
1
24913 

Length

Max length2
Median length2
Mean length1.877033
Min length1

Characters and Unicode

Total characters380285
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row1
5th row-1

Common Values

ValueCountFrequency (%)
-1 177686
87.7%
1 24913
 
12.3%

Length

2023-06-17T11:45:58.025614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:58.117188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
53.3%
- 177686
46.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
53.3%
Dash Punctuation 177686
46.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 177686
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 380285
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
53.3%
- 177686
46.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 380285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
53.3%
- 177686
46.7%

Wearing_Necktie
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
-1
187867 
1
 
14732

Length

Max length2
Median length2
Mean length1.9272849
Min length1

Characters and Unicode

Total characters390466
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 187867
92.7%
1 14732
 
7.3%

Length

2023-06-17T11:45:58.195700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:58.287273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
51.9%
- 187867
48.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
51.9%
Dash Punctuation 187867
48.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 187867
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 390466
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
51.9%
- 187867
48.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 390466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
51.9%
- 187867
48.1%

Young
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
1
156734 
-1
45865 

Length

Max length2
Median length1
Mean length1.2263832
Min length1

Characters and Unicode

Total characters248464
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 156734
77.4%
-1 45865
 
22.6%

Length

2023-06-17T11:45:58.363776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:45:58.457437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 202599
100.0%

Most occurring characters

ValueCountFrequency (%)
1 202599
81.5%
- 45865
 
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
81.5%
Dash Punctuation 45865
 
18.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 248464
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202599
81.5%
- 45865
 
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 248464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202599
81.5%
- 45865
 
18.5%

x_1_x
Real number (ℝ)

Distinct1550
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.76456
Minimum1
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:45:58.544595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q169
median110
Q3181
95-th percentile461
Maximum3840
Range3839
Interquartile range (IQR)112

Descriptive statistics

Standard deviation164.51813
Coefficient of variation (CV)1.04946
Kurtosis28.123597
Mean156.76456
Median Absolute Deviation (MAD)50
Skewness3.9300001
Sum31760344
Variance27066.217
MonotonicityNot monotonic
2023-06-17T11:45:58.645721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 1333
 
0.7%
90 1330
 
0.7%
80 1328
 
0.7%
79 1322
 
0.7%
92 1299
 
0.6%
86 1298
 
0.6%
81 1297
 
0.6%
95 1296
 
0.6%
85 1294
 
0.6%
73 1274
 
0.6%
Other values (1540) 189528
93.5%
ValueCountFrequency (%)
1 1178
0.6%
2 131
 
0.1%
3 188
 
0.1%
4 182
 
0.1%
5 213
 
0.1%
6 239
 
0.1%
7 234
 
0.1%
8 252
 
0.1%
9 305
 
0.2%
10 326
 
0.2%
ValueCountFrequency (%)
3840 1
< 0.1%
3815 1
< 0.1%
3688 1
< 0.1%
3601 1
< 0.1%
3538 1
< 0.1%
3496 1
< 0.1%
3063 1
< 0.1%
3013 1
< 0.1%
2927 1
< 0.1%
2810 1
< 0.1%

y_1_x
Real number (ℝ)

Distinct901
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.335505
Minimum0
Maximum1858
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:45:58.752606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q144
median68
Q398
95-th percentile204
Maximum1858
Range1858
Interquartile range (IQR)54

Descriptive statistics

Standard deviation76.067284
Coefficient of variation (CV)0.90196038
Kurtosis35.803507
Mean84.335505
Median Absolute Deviation (MAD)26
Skewness4.4886367
Sum17086289
Variance5786.2318
MonotonicityNot monotonic
2023-06-17T11:45:58.849340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 2262
 
1.1%
50 2252
 
1.1%
60 2216
 
1.1%
57 2203
 
1.1%
51 2198
 
1.1%
55 2196
 
1.1%
46 2184
 
1.1%
58 2183
 
1.1%
53 2179
 
1.1%
44 2176
 
1.1%
Other values (891) 180550
89.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1876
0.9%
2 134
 
0.1%
3 174
 
0.1%
4 199
 
0.1%
5 217
 
0.1%
6 241
 
0.1%
7 293
 
0.1%
8 290
 
0.1%
9 347
 
0.2%
ValueCountFrequency (%)
1858 1
< 0.1%
1724 1
< 0.1%
1710 1
< 0.1%
1690 1
< 0.1%
1555 1
< 0.1%
1478 1
< 0.1%
1421 1
< 0.1%
1404 1
< 0.1%
1364 1
< 0.1%
1329 1
< 0.1%

width_x
Real number (ℝ)

Distinct1016
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.75406
Minimum0
Maximum3827
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:45:58.958469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75
Q1120
median164
Q3221
95-th percentile419
Maximum3827
Range3827
Interquartile range (IQR)101

Descriptive statistics

Standard deviation141.77007
Coefficient of variation (CV)0.72794408
Kurtosis31.54556
Mean194.75406
Median Absolute Deviation (MAD)50
Skewness4.3007597
Sum39456978
Variance20098.752
MonotonicityNot monotonic
2023-06-17T11:45:59.059717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 3140
 
1.5%
140 3063
 
1.5%
125 3018
 
1.5%
156 3015
 
1.5%
143 3010
 
1.5%
130 3001
 
1.5%
138 2998
 
1.5%
146 2975
 
1.5%
135 2958
 
1.5%
148 2947
 
1.5%
Other values (1006) 172474
85.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
3 3
 
< 0.1%
5 3
 
< 0.1%
8 4
 
< 0.1%
10 7
< 0.1%
13 5
 
< 0.1%
16 15
< 0.1%
18 6
 
< 0.1%
21 8
< 0.1%
23 8
< 0.1%
ValueCountFrequency (%)
3827 1
< 0.1%
3076 1
< 0.1%
2603 2
< 0.1%
2532 2
< 0.1%
2509 1
< 0.1%
2486 1
< 0.1%
2478 1
< 0.1%
2402 1
< 0.1%
2384 1
< 0.1%
2366 1
< 0.1%

height_x
Real number (ℝ)

Distinct1249
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.92233
Minimum0
Maximum5299
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:45:59.165663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile104
Q1166
median227
Q3306
95-th percentile576
Maximum5299
Range5299
Interquartile range (IQR)140

Descriptive statistics

Standard deviation195.66494
Coefficient of variation (CV)0.72758903
Kurtosis31.708619
Mean268.92233
Median Absolute Deviation (MAD)68
Skewness4.3188581
Sum54483395
Variance38284.767
MonotonicityNot monotonic
2023-06-17T11:45:59.273840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184 3090
 
1.5%
194 3034
 
1.5%
216 2963
 
1.5%
191 2962
 
1.5%
173 2961
 
1.5%
198 2950
 
1.5%
180 2947
 
1.5%
202 2931
 
1.4%
187 2931
 
1.4%
205 2913
 
1.4%
Other values (1239) 172917
85.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
4 3
 
< 0.1%
7 3
 
< 0.1%
11 4
 
< 0.1%
14 7
< 0.1%
18 5
 
< 0.1%
22 15
< 0.1%
25 6
 
< 0.1%
29 8
< 0.1%
32 8
< 0.1%
ValueCountFrequency (%)
5299 1
< 0.1%
4259 1
< 0.1%
3604 2
< 0.1%
3506 2
< 0.1%
3442 1
< 0.1%
3431 1
< 0.1%
3326 1
< 0.1%
3283 1
< 0.1%
3276 1
< 0.1%
3187 1
< 0.1%

x_1_y
Real number (ℝ)

Distinct1550
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.76456
Minimum1
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:45:59.859444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q169
median110
Q3181
95-th percentile461
Maximum3840
Range3839
Interquartile range (IQR)112

Descriptive statistics

Standard deviation164.51813
Coefficient of variation (CV)1.04946
Kurtosis28.123597
Mean156.76456
Median Absolute Deviation (MAD)50
Skewness3.9300001
Sum31760344
Variance27066.217
MonotonicityNot monotonic
2023-06-17T11:45:59.984268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 1333
 
0.7%
90 1330
 
0.7%
80 1328
 
0.7%
79 1322
 
0.7%
92 1299
 
0.6%
86 1298
 
0.6%
81 1297
 
0.6%
95 1296
 
0.6%
85 1294
 
0.6%
73 1274
 
0.6%
Other values (1540) 189528
93.5%
ValueCountFrequency (%)
1 1178
0.6%
2 131
 
0.1%
3 188
 
0.1%
4 182
 
0.1%
5 213
 
0.1%
6 239
 
0.1%
7 234
 
0.1%
8 252
 
0.1%
9 305
 
0.2%
10 326
 
0.2%
ValueCountFrequency (%)
3840 1
< 0.1%
3815 1
< 0.1%
3688 1
< 0.1%
3601 1
< 0.1%
3538 1
< 0.1%
3496 1
< 0.1%
3063 1
< 0.1%
3013 1
< 0.1%
2927 1
< 0.1%
2810 1
< 0.1%

y_1_y
Real number (ℝ)

Distinct901
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.335505
Minimum0
Maximum1858
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:00.100547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q144
median68
Q398
95-th percentile204
Maximum1858
Range1858
Interquartile range (IQR)54

Descriptive statistics

Standard deviation76.067284
Coefficient of variation (CV)0.90196038
Kurtosis35.803507
Mean84.335505
Median Absolute Deviation (MAD)26
Skewness4.4886367
Sum17086289
Variance5786.2318
MonotonicityNot monotonic
2023-06-17T11:46:00.205499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 2262
 
1.1%
50 2252
 
1.1%
60 2216
 
1.1%
57 2203
 
1.1%
51 2198
 
1.1%
55 2196
 
1.1%
46 2184
 
1.1%
58 2183
 
1.1%
53 2179
 
1.1%
44 2176
 
1.1%
Other values (891) 180550
89.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1876
0.9%
2 134
 
0.1%
3 174
 
0.1%
4 199
 
0.1%
5 217
 
0.1%
6 241
 
0.1%
7 293
 
0.1%
8 290
 
0.1%
9 347
 
0.2%
ValueCountFrequency (%)
1858 1
< 0.1%
1724 1
< 0.1%
1710 1
< 0.1%
1690 1
< 0.1%
1555 1
< 0.1%
1478 1
< 0.1%
1421 1
< 0.1%
1404 1
< 0.1%
1364 1
< 0.1%
1329 1
< 0.1%

width_y
Real number (ℝ)

Distinct1016
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.75406
Minimum0
Maximum3827
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:00.320978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75
Q1120
median164
Q3221
95-th percentile419
Maximum3827
Range3827
Interquartile range (IQR)101

Descriptive statistics

Standard deviation141.77007
Coefficient of variation (CV)0.72794408
Kurtosis31.54556
Mean194.75406
Median Absolute Deviation (MAD)50
Skewness4.3007597
Sum39456978
Variance20098.752
MonotonicityNot monotonic
2023-06-17T11:46:00.432556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 3140
 
1.5%
140 3063
 
1.5%
125 3018
 
1.5%
156 3015
 
1.5%
143 3010
 
1.5%
130 3001
 
1.5%
138 2998
 
1.5%
146 2975
 
1.5%
135 2958
 
1.5%
148 2947
 
1.5%
Other values (1006) 172474
85.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
3 3
 
< 0.1%
5 3
 
< 0.1%
8 4
 
< 0.1%
10 7
< 0.1%
13 5
 
< 0.1%
16 15
< 0.1%
18 6
 
< 0.1%
21 8
< 0.1%
23 8
< 0.1%
ValueCountFrequency (%)
3827 1
< 0.1%
3076 1
< 0.1%
2603 2
< 0.1%
2532 2
< 0.1%
2509 1
< 0.1%
2486 1
< 0.1%
2478 1
< 0.1%
2402 1
< 0.1%
2384 1
< 0.1%
2366 1
< 0.1%

height_y
Real number (ℝ)

Distinct1249
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.92233
Minimum0
Maximum5299
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:00.553471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile104
Q1166
median227
Q3306
95-th percentile576
Maximum5299
Range5299
Interquartile range (IQR)140

Descriptive statistics

Standard deviation195.66494
Coefficient of variation (CV)0.72758903
Kurtosis31.708619
Mean268.92233
Median Absolute Deviation (MAD)68
Skewness4.3188581
Sum54483395
Variance38284.767
MonotonicityNot monotonic
2023-06-17T11:46:00.673561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184 3090
 
1.5%
194 3034
 
1.5%
216 2963
 
1.5%
191 2962
 
1.5%
173 2961
 
1.5%
198 2950
 
1.5%
180 2947
 
1.5%
202 2931
 
1.4%
187 2931
 
1.4%
205 2913
 
1.4%
Other values (1239) 172917
85.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
4 3
 
< 0.1%
7 3
 
< 0.1%
11 4
 
< 0.1%
14 7
< 0.1%
18 5
 
< 0.1%
22 15
< 0.1%
25 6
 
< 0.1%
29 8
< 0.1%
32 8
< 0.1%
ValueCountFrequency (%)
5299 1
< 0.1%
4259 1
< 0.1%
3604 2
< 0.1%
3506 2
< 0.1%
3442 1
< 0.1%
3431 1
< 0.1%
3326 1
< 0.1%
3283 1
< 0.1%
3276 1
< 0.1%
3187 1
< 0.1%

partition
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
0
162770 
2
19962 
1
19867 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters202599
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 162770
80.3%
2 19962
 
9.9%
1 19867
 
9.8%

Length

2023-06-17T11:46:00.773035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-17T11:46:00.865013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 162770
80.3%
2 19962
 
9.9%
1 19867
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 162770
80.3%
2 19962
 
9.9%
1 19867
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 162770
80.3%
2 19962
 
9.9%
1 19867
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 202599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 162770
80.3%
2 19962
 
9.9%
1 19867
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 202599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 162770
80.3%
2 19962
 
9.9%
1 19867
 
9.8%

lefteye_x
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.353867
Minimum56
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:00.946106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile67
Q168
median69
Q370
95-th percentile72
Maximum88
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7179517
Coefficient of variation (CV)0.024770814
Kurtosis7.9267312
Mean69.353867
Median Absolute Deviation (MAD)1
Skewness1.5574585
Sum14051024
Variance2.9513581
MonotonicityNot monotonic
2023-06-17T11:46:01.030997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
69 57572
28.4%
70 46522
23.0%
68 41033
20.3%
71 22194
 
11.0%
67 16132
 
8.0%
72 8278
 
4.1%
73 3308
 
1.6%
66 2991
 
1.5%
74 1479
 
0.7%
75 842
 
0.4%
Other values (21) 2248
 
1.1%
ValueCountFrequency (%)
56 3
 
< 0.1%
59 1
 
< 0.1%
60 7
 
< 0.1%
61 5
 
< 0.1%
62 15
 
< 0.1%
63 41
 
< 0.1%
64 106
 
0.1%
65 409
 
0.2%
66 2991
 
1.5%
67 16132
8.0%
ValueCountFrequency (%)
88 1
 
< 0.1%
87 2
 
< 0.1%
86 10
 
< 0.1%
85 13
 
< 0.1%
84 33
 
< 0.1%
83 48
 
< 0.1%
82 59
 
< 0.1%
81 90
< 0.1%
80 124
0.1%
79 205
0.1%

lefteye_y
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.19798
Minimum98
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:01.121457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile109
Q1111
median111
Q3112
95-th percentile113
Maximum129
Range31
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1292842
Coefficient of variation (CV)0.010155618
Kurtosis6.7330655
Mean111.19798
Median Absolute Deviation (MAD)1
Skewness-0.91054288
Sum22528600
Variance1.2752828
MonotonicityNot monotonic
2023-06-17T11:46:01.201606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
111 86618
42.8%
112 63290
31.2%
110 27396
 
13.5%
113 11992
 
5.9%
109 6502
 
3.2%
108 2255
 
1.1%
114 2030
 
1.0%
107 968
 
0.5%
106 457
 
0.2%
115 418
 
0.2%
Other values (17) 673
 
0.3%
ValueCountFrequency (%)
98 1
 
< 0.1%
99 4
 
< 0.1%
100 3
 
< 0.1%
101 8
 
< 0.1%
102 22
 
< 0.1%
103 44
 
< 0.1%
104 129
 
0.1%
105 225
 
0.1%
106 457
0.2%
107 968
0.5%
ValueCountFrequency (%)
129 1
 
< 0.1%
128 1
 
< 0.1%
124 1
 
< 0.1%
121 3
 
< 0.1%
120 4
 
< 0.1%
119 9
 
< 0.1%
118 23
 
< 0.1%
117 52
 
< 0.1%
116 143
 
0.1%
115 418
0.2%

righteye_x
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.64403
Minimum90
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:01.292807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile105
Q1107
median108
Q3109
95-th percentile110
Maximum124
Range34
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6902522
Coefficient of variation (CV)0.015702238
Kurtosis8.0327696
Mean107.64403
Median Absolute Deviation (MAD)1
Skewness-1.534525
Sum21808573
Variance2.8569525
MonotonicityNot monotonic
2023-06-17T11:46:01.381580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
108 58536
28.9%
107 47429
23.4%
109 40771
20.1%
106 22202
 
11.0%
110 15252
 
7.5%
105 8057
 
4.0%
104 3069
 
1.5%
111 2860
 
1.4%
103 1384
 
0.7%
102 792
 
0.4%
Other values (22) 2247
 
1.1%
ValueCountFrequency (%)
90 3
 
< 0.1%
91 10
 
< 0.1%
92 17
 
< 0.1%
93 18
 
< 0.1%
94 37
 
< 0.1%
95 69
 
< 0.1%
96 81
 
< 0.1%
97 131
0.1%
98 157
0.1%
99 250
0.1%
ValueCountFrequency (%)
124 1
 
< 0.1%
122 1
 
< 0.1%
121 1
 
< 0.1%
120 1
 
< 0.1%
117 7
 
< 0.1%
116 10
 
< 0.1%
115 27
 
< 0.1%
114 27
 
< 0.1%
113 86
 
< 0.1%
112 452
0.2%

righteye_y
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.1616
Minimum95
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:01.471953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile109
Q1111
median111
Q3112
95-th percentile113
Maximum122
Range27
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1692294
Coefficient of variation (CV)0.010518285
Kurtosis6.2844488
Mean111.1616
Median Absolute Deviation (MAD)1
Skewness-1.1037703
Sum22521229
Variance1.3670975
MonotonicityNot monotonic
2023-06-17T11:46:01.558872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
111 83850
41.4%
112 62759
31.0%
110 28686
 
14.2%
113 12568
 
6.2%
109 7553
 
3.7%
108 2736
 
1.4%
114 1727
 
0.9%
107 1093
 
0.5%
106 541
 
0.3%
115 349
 
0.2%
Other values (15) 737
 
0.4%
ValueCountFrequency (%)
95 1
 
< 0.1%
99 4
 
< 0.1%
100 5
 
< 0.1%
101 12
 
< 0.1%
102 31
 
< 0.1%
103 72
 
< 0.1%
104 140
 
0.1%
105 282
 
0.1%
106 541
0.3%
107 1093
0.5%
ValueCountFrequency (%)
122 2
 
< 0.1%
121 3
 
< 0.1%
120 4
 
< 0.1%
119 13
 
< 0.1%
118 17
 
< 0.1%
117 45
 
< 0.1%
116 106
 
0.1%
115 349
 
0.2%
114 1727
 
0.9%
113 12568
6.2%

nose_x
Real number (ℝ)

Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.06314
Minimum57
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:01.657525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile77
Q184
median88
Q392
95-th percentile99
Maximum121
Range64
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.6477329
Coefficient of variation (CV)0.075488257
Kurtosis0.78752243
Mean88.06314
Median Absolute Deviation (MAD)4
Skewness0.047707358
Sum17841504
Variance44.192353
MonotonicityNot monotonic
2023-06-17T11:46:01.756990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 17621
 
8.7%
89 17027
 
8.4%
87 15319
 
7.6%
90 14090
 
7.0%
86 12280
 
6.1%
91 11358
 
5.6%
85 10033
 
5.0%
92 8992
 
4.4%
84 8455
 
4.2%
83 7420
 
3.7%
Other values (55) 80004
39.5%
ValueCountFrequency (%)
57 1
 
< 0.1%
58 2
 
< 0.1%
59 2
 
< 0.1%
60 1
 
< 0.1%
61 9
 
< 0.1%
62 16
 
< 0.1%
63 38
 
< 0.1%
64 38
 
< 0.1%
65 64
< 0.1%
66 116
0.1%
ValueCountFrequency (%)
121 2
 
< 0.1%
120 1
 
< 0.1%
119 3
 
< 0.1%
118 1
 
< 0.1%
117 10
 
< 0.1%
116 18
 
< 0.1%
115 26
 
< 0.1%
114 37
< 0.1%
113 49
< 0.1%
112 82
< 0.1%

nose_y
Real number (ℝ)

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.10202
Minimum93
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:01.866172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum93
5-th percentile128
Q1133
median135
Q3138
95-th percentile141
Maximum156
Range63
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.245078
Coefficient of variation (CV)0.031421276
Kurtosis1.4108747
Mean135.10202
Median Absolute Deviation (MAD)3
Skewness-0.65043106
Sum27371535
Variance18.020687
MonotonicityNot monotonic
2023-06-17T11:46:01.958392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136 21254
10.5%
137 20433
10.1%
135 20237
10.0%
134 18143
9.0%
138 17765
8.8%
133 15554
 
7.7%
139 14319
 
7.1%
132 12480
 
6.2%
140 10577
 
5.2%
131 9469
 
4.7%
Other values (45) 42368
20.9%
ValueCountFrequency (%)
93 1
 
< 0.1%
100 2
 
< 0.1%
102 2
 
< 0.1%
104 1
 
< 0.1%
105 2
 
< 0.1%
106 1
 
< 0.1%
107 1
 
< 0.1%
108 4
< 0.1%
109 8
< 0.1%
110 7
< 0.1%
ValueCountFrequency (%)
156 1
 
< 0.1%
154 1
 
< 0.1%
153 2
 
< 0.1%
152 2
 
< 0.1%
151 5
 
< 0.1%
150 6
 
< 0.1%
149 24
 
< 0.1%
148 59
 
< 0.1%
147 129
0.1%
146 271
0.1%

leftmouth_x
Real number (ℝ)

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.247459
Minimum57
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:02.054518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile66
Q169
median72
Q373
95-th percentile76
Maximum90
Range33
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1680107
Coefficient of variation (CV)0.044464893
Kurtosis0.070618119
Mean71.247459
Median Absolute Deviation (MAD)2
Skewness-0.063421025
Sum14434664
Variance10.036292
MonotonicityNot monotonic
2023-06-17T11:46:02.144355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
73 26372
13.0%
72 25824
12.7%
71 22193
11.0%
74 22062
10.9%
70 19044
9.4%
69 16556
8.2%
68 14792
7.3%
75 13991
6.9%
67 12178
6.0%
66 8318
 
4.1%
Other values (24) 21269
10.5%
ValueCountFrequency (%)
57 3
 
< 0.1%
58 5
 
< 0.1%
59 2
 
< 0.1%
60 19
 
< 0.1%
61 56
 
< 0.1%
62 180
 
0.1%
63 645
 
0.3%
64 1887
 
0.9%
65 4545
2.2%
66 8318
4.1%
ValueCountFrequency (%)
90 1
 
< 0.1%
89 2
 
< 0.1%
88 9
 
< 0.1%
87 14
 
< 0.1%
86 34
 
< 0.1%
85 43
 
< 0.1%
84 62
 
< 0.1%
83 70
 
< 0.1%
82 143
0.1%
81 261
0.1%

leftmouth_y
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.11301
Minimum116
Maximum174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:02.233944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum116
5-th percentile150
Q1151
median152
Q3153
95-th percentile155
Maximum174
Range58
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7993431
Coefficient of variation (CV)0.011828989
Kurtosis6.9298047
Mean152.11301
Median Absolute Deviation (MAD)1
Skewness1.225925
Sum30817944
Variance3.2376356
MonotonicityNot monotonic
2023-06-17T11:46:02.316478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
152 54751
27.0%
151 43767
21.6%
153 40326
19.9%
150 22155
10.9%
154 18801
 
9.3%
155 7360
 
3.6%
149 6583
 
3.2%
156 3195
 
1.6%
157 1494
 
0.7%
148 1392
 
0.7%
Other values (23) 2775
 
1.4%
ValueCountFrequency (%)
116 1
 
< 0.1%
140 2
 
< 0.1%
141 1
 
< 0.1%
142 2
 
< 0.1%
143 5
 
< 0.1%
144 8
 
< 0.1%
145 30
 
< 0.1%
146 77
 
< 0.1%
147 319
 
0.2%
148 1392
0.7%
ValueCountFrequency (%)
174 1
 
< 0.1%
170 1
 
< 0.1%
169 3
 
< 0.1%
168 9
 
< 0.1%
167 34
 
< 0.1%
166 41
 
< 0.1%
165 52
 
< 0.1%
164 71
< 0.1%
163 112
0.1%
162 165
0.1%

rightmouth_x
Real number (ℝ)

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.58643
Minimum82
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:02.410337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile101
Q1103
median105
Q3108
95-th percentile111
Maximum120
Range38
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2331248
Coefficient of variation (CV)0.030620647
Kurtosis0.082260245
Mean105.58643
Median Absolute Deviation (MAD)2
Skewness0.033741758
Sum21391705
Variance10.453096
MonotonicityNot monotonic
2023-06-17T11:46:02.508767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
104 26081
12.9%
105 24589
12.1%
103 22891
11.3%
106 21178
10.5%
107 18250
9.0%
108 16187
8.0%
102 15596
7.7%
109 14319
7.1%
110 11918
5.9%
101 8396
 
4.1%
Other values (27) 23194
11.4%
ValueCountFrequency (%)
82 1
 
< 0.1%
83 1
 
< 0.1%
85 1
 
< 0.1%
86 2
 
< 0.1%
88 3
 
< 0.1%
89 9
 
< 0.1%
90 20
 
< 0.1%
91 42
< 0.1%
92 48
< 0.1%
93 68
< 0.1%
ValueCountFrequency (%)
120 2
 
< 0.1%
119 1
 
< 0.1%
118 4
 
< 0.1%
117 11
 
< 0.1%
116 40
 
< 0.1%
115 160
 
0.1%
114 580
 
0.3%
113 1739
 
0.9%
112 4208
2.1%
111 8124
4.0%

rightmouth_y
Real number (ℝ)

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.19466
Minimum114
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-06-17T11:46:02.605602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum114
5-th percentile150
Q1151
median152
Q3153
95-th percentile155
Maximum173
Range59
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7523685
Coefficient of variation (CV)0.011513994
Kurtosis7.2344185
Mean152.19466
Median Absolute Deviation (MAD)1
Skewness1.1360746
Sum30834486
Variance3.0707953
MonotonicityNot monotonic
2023-06-17T11:46:02.692399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
152 56327
27.8%
153 43140
21.3%
151 42294
20.9%
154 19900
 
9.8%
150 19155
 
9.5%
155 7930
 
3.9%
149 5425
 
2.7%
156 3162
 
1.6%
157 1497
 
0.7%
148 1230
 
0.6%
Other values (26) 2539
 
1.3%
ValueCountFrequency (%)
114 1
 
< 0.1%
138 1
 
< 0.1%
139 2
 
< 0.1%
140 1
 
< 0.1%
141 1
 
< 0.1%
142 2
 
< 0.1%
143 9
 
< 0.1%
144 14
 
< 0.1%
145 31
 
< 0.1%
146 82
< 0.1%
ValueCountFrequency (%)
173 1
 
< 0.1%
172 1
 
< 0.1%
170 1
 
< 0.1%
169 7
 
< 0.1%
168 2
 
< 0.1%
167 24
 
< 0.1%
166 31
 
< 0.1%
165 47
< 0.1%
164 87
< 0.1%
163 112
0.1%

Interactions

2023-06-17T11:45:45.826366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:04.639704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:06.727442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:09.592306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:12.015395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-17T11:45:17.198179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-17T11:45:24.170427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:26.382564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:28.490275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:30.809955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:32.931146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:35.285441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:37.441718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:39.537948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:41.945856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:44.856174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:47.607524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:06.020835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:08.690478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:11.231846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:13.455957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:16.361894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:19.106327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:21.298961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:24.284613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:26.496108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:28.603006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:30.923916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:33.042326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:35.408202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:37.546965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:39.652246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:42.063573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:44.993199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:47.723239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:06.143866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:08.823854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:11.362425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:13.569740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:16.500677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:19.224846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:21.424345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:24.438021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:26.611827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:28.725837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:31.054223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:33.446173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:35.526970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:37.658463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:39.767172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:42.184297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:45.135708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:47.840649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:06.264311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:08.978945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:11.484514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:13.682357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:16.637934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:19.343860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:21.557188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:24.558630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:26.729378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:28.865589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:31.165635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:33.554767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:35.643970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:37.778691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:39.880589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:42.297610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:45.282864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:47.958132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:06.380022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:09.140108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:11.610954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:13.803951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:16.769346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:19.469003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:21.697168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:24.677222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:26.842108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:29.012973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:31.278418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:33.670409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:35.758984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:37.891340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:40.002676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:42.411957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:45.406903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:48.066893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:06.494249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:09.338089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:11.738317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:13.959950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:16.903288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:19.590224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:21.818505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:24.789671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:26.951851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:29.150940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:31.394182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:33.780819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:35.878470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:38.004763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:40.122714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:42.529460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:45.546676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:48.183880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:06.612200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:09.474292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:11.879673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:14.120196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:17.037010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:19.709905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:21.972209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:24.915805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:27.071949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:29.289719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:31.537892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:33.895485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:35.995095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:38.120030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:40.243532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:42.678638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-17T11:45:45.686465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-17T11:46:02.838326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
x_1_xy_1_xwidth_xheight_xx_1_yy_1_ywidth_yheight_ylefteye_xlefteye_yrighteye_xrighteye_ynose_xnose_yleftmouth_xleftmouth_yrightmouth_xrightmouth_y5_o_Clock_ShadowArched_EyebrowsAttractiveBags_Under_EyesBaldBangsBig_LipsBig_NoseBlack_HairBlond_HairBlurryBrown_HairBushy_EyebrowsChubbyDouble_ChinEyeglassesGoateeGray_HairHeavy_MakeupHigh_CheekbonesMaleMouth_Slightly_OpenMustacheNarrow_EyesNo_BeardOval_FacePale_SkinPointy_NoseReceding_HairlineRosy_CheeksSideburnsSmilingStraight_HairWavy_HairWearing_EarringsWearing_HatWearing_LipstickWearing_NecklaceWearing_NecktieYoungpartition
x_1_x1.0000.2960.2130.2191.0000.2960.2130.219-0.0030.0340.020-0.0130.2170.0150.0400.033-0.054-0.0570.0390.1210.0800.0130.0280.0120.0970.0320.0330.0560.0420.0310.0270.0350.0350.0400.0370.0400.0990.0500.1390.0290.0200.0210.0620.0890.0170.0440.0270.0140.0250.0630.0130.0560.0500.0240.1360.0620.0510.0780.008
y_1_x0.2961.0000.4990.5000.2961.0000.4990.500-0.2040.1340.2050.139-0.0340.305-0.173-0.1830.170-0.1690.0170.0980.0560.0360.0240.0270.0940.0250.0230.0480.0410.0160.0050.0230.0170.0350.0290.0260.0770.0300.1000.0080.0130.0180.0440.0750.0220.0370.0120.0700.0140.0220.0100.0400.0910.0190.1030.0720.0310.0410.000
width_x0.2130.4991.0000.9980.2130.4991.0000.9980.062-0.062-0.073-0.028-0.0110.0390.0880.050-0.0780.0590.0130.0900.0550.0370.0240.0260.1010.0290.0250.0460.0520.0190.0120.0150.0170.0310.0150.0230.0690.0280.0840.0250.0090.0050.0260.0820.0280.0400.0100.0570.0040.0430.0090.0300.0810.0130.0910.0630.0240.0400.008
height_x0.2190.5000.9981.0000.2190.5000.9981.0000.062-0.064-0.074-0.028-0.0060.0390.0870.051-0.0770.0590.0130.0900.0560.0360.0240.0270.1010.0290.0250.0460.0510.0200.0120.0150.0170.0310.0150.0240.0700.0280.0850.0250.0100.0040.0260.0820.0280.0410.0110.0570.0030.0420.0090.0310.0820.0140.0920.0640.0240.0410.008
x_1_y1.0000.2960.2130.2191.0000.2960.2130.219-0.0030.0340.020-0.0130.2170.0150.0400.033-0.054-0.0570.0390.1210.0800.0130.0280.0120.0970.0320.0330.0560.0420.0310.0270.0350.0350.0400.0370.0400.0990.0500.1390.0290.0200.0210.0620.0890.0170.0440.0270.0140.0250.0630.0130.0560.0500.0240.1360.0620.0510.0780.008
y_1_y0.2961.0000.4990.5000.2961.0000.4990.500-0.2040.1340.2050.139-0.0340.305-0.173-0.1830.170-0.1690.0170.0980.0560.0360.0240.0270.0940.0250.0230.0480.0410.0160.0050.0230.0170.0350.0290.0260.0770.0300.1000.0080.0130.0180.0440.0750.0220.0370.0120.0700.0140.0220.0100.0400.0910.0190.1030.0720.0310.0410.000
width_y0.2130.4991.0000.9980.2130.4991.0000.9980.062-0.062-0.073-0.028-0.0110.0390.0880.050-0.0780.0590.0130.0900.0550.0370.0240.0260.1010.0290.0250.0460.0520.0190.0120.0150.0170.0310.0150.0230.0690.0280.0840.0250.0090.0050.0260.0820.0280.0400.0100.0570.0040.0430.0090.0300.0810.0130.0910.0630.0240.0400.008
height_y0.2190.5000.9981.0000.2190.5000.9981.0000.062-0.064-0.074-0.028-0.0060.0390.0870.051-0.0770.0590.0130.0900.0560.0360.0240.0270.1010.0290.0250.0460.0510.0200.0120.0150.0170.0310.0150.0240.0700.0280.0850.0250.0100.0040.0260.0820.0280.0410.0110.0570.0030.0420.0090.0310.0820.0140.0920.0640.0240.0410.008
lefteye_x-0.003-0.2040.0620.062-0.003-0.2040.0620.0621.000-0.425-0.851-0.7550.006-0.3090.5940.821-0.6870.6960.1000.1620.1730.0560.0160.0540.0690.0830.0100.0890.0810.0290.0550.0320.0510.0440.0640.0390.2170.4070.2000.2820.0510.0780.1110.1650.0320.0180.0290.1860.0700.4520.0270.1000.1490.0640.2330.1060.0630.0370.011
lefteye_y0.0340.134-0.062-0.0640.0340.134-0.062-0.064-0.4251.0000.729-0.0620.1050.163-0.543-0.3280.260-0.6620.0320.1020.1370.0400.0130.0350.0390.0550.0100.0460.0780.0390.0420.0280.0360.0400.0210.0270.1280.1810.1010.0760.0190.0370.0410.1310.0160.0140.0110.0800.0260.2100.0200.0580.0730.0450.1350.0650.0310.0370.007
righteye_x0.0200.205-0.073-0.0740.0200.205-0.073-0.074-0.8510.7291.0000.4590.0530.292-0.675-0.7060.589-0.8120.0920.1650.1820.0510.0200.0570.0680.0720.0100.0880.0750.0390.0410.0330.0600.0510.0620.0410.2190.3810.2050.2270.0490.0610.1080.1770.0210.0190.0260.1550.0710.4390.0240.0950.1420.0670.2340.1090.0580.0420.013
righteye_y-0.0130.139-0.028-0.028-0.0130.139-0.028-0.028-0.755-0.0620.4591.000-0.0750.225-0.310-0.6850.589-0.3490.0810.1320.1370.0480.0160.0450.0560.0700.0140.0750.0800.0200.0470.0260.0430.0330.0550.0280.1670.3060.1570.2040.0420.0710.0900.1200.0220.0150.0210.1490.0600.3450.0260.0770.1180.0510.1810.0810.0500.0250.009
nose_x0.217-0.034-0.011-0.0060.217-0.034-0.011-0.0060.0060.1050.053-0.0751.000-0.010-0.0270.142-0.013-0.1890.0590.0600.0800.0490.0200.0340.0320.1130.0280.0320.0670.0250.0710.0610.0530.0230.0300.0270.0700.0570.0710.0330.0330.0380.0500.1420.0280.0750.0430.0560.0360.0570.0240.0340.0310.0300.0780.0480.0580.0340.004
nose_y0.0150.3050.0390.0390.0150.3050.0390.039-0.3090.1630.2920.225-0.0101.000-0.344-0.2670.359-0.2440.0500.0630.2540.0570.0370.0630.0260.0420.0270.0650.0960.0540.0400.0830.0320.1080.0780.0240.1690.2850.1240.1160.0520.1100.0940.1010.0150.1500.0280.1410.0480.3290.0480.0790.0560.0900.1740.0480.0390.0630.006
leftmouth_x0.040-0.1730.0880.0870.040-0.1730.0880.0870.594-0.543-0.675-0.310-0.027-0.3441.0000.506-0.8930.5740.0670.1010.1370.1480.0240.0530.0570.1490.0170.0840.0590.0290.0460.0590.0990.0320.0590.0430.1470.5990.1200.4860.0450.1220.0880.1750.0690.0210.0380.2000.0690.7140.0130.0660.1510.0580.1510.1000.0590.0700.014
leftmouth_y0.033-0.1830.0500.0510.033-0.1830.0500.0510.821-0.328-0.706-0.6850.142-0.2670.5061.000-0.6080.3490.0740.1080.1220.0500.0070.0370.0490.0630.0120.0620.0710.0190.0490.0250.0350.0270.0480.0300.1520.2890.1450.2250.0350.0610.0770.1050.0290.0110.0220.1310.0530.3190.0240.0680.0990.0490.1640.0680.0470.0270.008
rightmouth_x-0.0540.170-0.078-0.077-0.0540.170-0.078-0.077-0.6870.2600.5890.589-0.0130.359-0.893-0.6081.000-0.4760.0660.1130.1470.1500.0260.0510.0600.1540.0180.0920.0700.0270.0490.0650.1010.0400.0590.0520.1550.5910.1240.4750.0450.1250.0900.1740.0670.0190.0410.2110.0700.6980.0120.0700.1550.0610.1590.1010.0660.0770.012
rightmouth_y-0.057-0.1690.0590.059-0.057-0.1690.0590.0590.696-0.662-0.812-0.349-0.189-0.2440.5740.349-0.4761.0000.0460.0800.1060.0350.0060.0310.0420.0560.0090.0380.0750.0230.0450.0270.0300.0250.0220.0160.1020.1500.0870.1050.0190.0520.0400.0820.0190.0110.0220.0460.0270.1660.0170.0480.0460.0370.1140.0470.0330.0270.008
5_o_Clock_Shadow0.0390.0170.0130.0130.0390.0170.0130.0130.1000.0320.0920.0810.0590.0500.0670.0740.0660.0461.0000.1590.0620.1680.0050.0890.0440.1520.1010.1330.0330.0120.2180.0100.0020.0070.1440.0440.2810.1610.4180.0660.0920.0110.5270.0850.0400.0240.0220.0900.2590.0680.0520.1240.1620.0340.3340.1160.0980.0150.013
Arched_Eyebrows0.1210.0980.0900.0900.1210.0980.0900.0900.1620.1020.1650.1320.0600.0630.1010.1080.1130.0800.1591.0000.2510.0920.0690.0280.2430.0840.0000.1270.0760.0180.0180.0890.0780.1490.1130.0990.4400.1560.4080.0670.0860.0240.2030.0130.0480.1530.0170.2240.1160.0940.0540.2010.2950.1000.4600.2200.1330.1470.014
Attractive0.0800.0560.0550.0560.0800.0560.0550.0560.1730.1370.1820.1370.0800.2540.1370.1220.1470.1060.0620.2511.0000.1780.1460.0600.0630.2770.0030.1550.1810.1320.0420.2370.2090.2230.1470.2020.4770.1490.3940.0210.1400.0740.1980.1940.0860.2280.1790.1640.1000.1480.0410.2150.1240.1390.4800.0690.1560.3880.011
Bags_Under_Eyes0.0130.0360.0370.0360.0130.0360.0370.0360.0560.0400.0510.0480.0490.0570.1480.0500.1500.0350.1680.0920.1781.0000.1160.0580.0060.3620.0010.1070.0330.0460.1070.1570.1960.0420.0950.1720.2930.0730.3010.0590.1100.1070.1470.1360.0330.1140.1170.0940.1000.1130.0240.1270.0970.0050.2840.0530.1970.2310.000
Bald0.0280.0240.0240.0240.0280.0240.0240.0240.0160.0130.0200.0160.0200.0370.0240.0070.0260.0060.0050.0690.1460.1161.0000.0640.0030.1800.0800.0630.0090.0770.0200.2240.2130.1090.1150.1510.1200.0000.1780.0000.0800.0120.1160.0110.0230.0560.1400.0370.0580.0090.0720.1030.0610.0300.1430.0500.1760.1960.004
Bangs0.0120.0270.0260.0270.0120.0270.0260.0270.0540.0350.0570.0450.0340.0630.0530.0370.0510.0310.0890.0280.0600.0580.0641.0000.0350.0690.0330.0990.0090.0690.0720.0840.0690.0590.0860.0620.1200.0530.1630.0090.0670.0130.1320.0000.0420.0110.1240.0610.0730.0520.0200.0680.0580.0800.1630.1140.0930.0180.005
Big_Lips0.0970.0940.1010.1010.0970.0940.1010.1010.0690.0390.0680.0560.0320.0260.0570.0490.0600.0420.0440.2430.0630.0060.0030.0351.0000.0770.0670.0240.0370.0150.0210.0070.0090.0500.0190.0880.1470.0450.1670.0490.0310.1140.0230.1120.0390.0530.0210.0770.0400.0130.0370.1200.1260.0130.1950.1500.0680.1080.090
Big_Nose0.0320.0250.0290.0290.0320.0250.0290.0290.0830.0550.0720.0700.1130.0420.1490.0630.1540.0560.1520.0840.2770.3620.1800.0690.0771.0000.0810.1580.0380.1330.1410.3140.2980.1400.1940.1960.2780.0590.3690.0590.2080.0700.2540.1060.0510.1590.2020.0550.1320.1010.0290.1320.0580.0650.3040.0360.2050.2840.020
Black_Hair0.0330.0230.0250.0250.0330.0230.0250.0250.0100.0100.0100.0140.0280.0270.0170.0120.0180.0090.1010.0000.0030.0010.0800.0330.0670.0811.0000.2340.0380.2520.2580.0090.0280.0160.0590.1170.0490.0080.1160.0230.0640.0110.0990.0320.0390.0470.0000.0400.0430.0010.1120.0850.0010.1040.0670.0420.0230.1210.033
Blond_Hair0.0560.0480.0460.0460.0560.0480.0460.0460.0890.0460.0880.0750.0320.0650.0840.0620.0920.0380.1330.1270.1550.1070.0630.0990.0240.1580.2341.0000.0100.1700.1500.0900.0770.0810.1020.0520.2470.1210.3030.0700.0860.0000.1710.0500.0580.1150.0700.1420.0960.0900.0050.1280.0950.0830.2820.1440.1060.0520.014
Blurry0.0420.0410.0520.0510.0420.0410.0520.0510.0810.0780.0750.0800.0670.0960.0590.0710.0700.0750.0330.0760.1810.0330.0090.0090.0370.0380.0380.0101.0000.0400.0650.0090.0110.0140.0260.0070.1400.0770.0240.0170.0040.0710.0040.0830.0200.0550.0070.0570.0250.0530.0370.0190.0580.0160.1270.0040.0150.0660.004
Brown_Hair0.0310.0160.0190.0200.0310.0160.0190.0200.0290.0390.0390.0200.0250.0540.0290.0190.0270.0230.0120.0180.1320.0460.0770.0690.0150.1330.2520.1700.0401.0000.0640.0930.0790.0780.0660.1030.0900.0220.1120.0070.0690.0230.0780.0460.0160.0470.1010.0120.0350.0230.0160.1540.0000.0980.1010.0040.0730.1030.034
Bushy_Eyebrows0.0270.0050.0120.0120.0270.0050.0120.0120.0550.0420.0410.0470.0710.0400.0460.0490.0490.0450.2180.0180.0420.1070.0200.0720.0210.1410.2580.1500.0650.0641.0000.0030.0000.0740.1090.0510.1260.0490.2470.0320.1050.0130.2030.0180.0220.0140.0320.0310.1280.0020.0740.0620.0760.0200.1720.0710.0630.0850.012
Chubby0.0350.0230.0150.0150.0350.0230.0150.0150.0320.0280.0330.0260.0610.0830.0590.0250.0650.0270.0100.0890.2370.1570.2240.0840.0070.3140.0090.0900.0090.0930.0031.0000.5340.1710.1620.2110.1660.0370.2300.0250.1830.0430.1690.0190.0340.1240.1870.0430.1160.0360.0330.0970.0560.0590.1930.0490.1960.2950.007
Double_Chin0.0350.0170.0170.0170.0350.0170.0170.0170.0510.0360.0600.0430.0530.0320.0990.0350.1010.0300.0020.0780.2090.1960.2130.0690.0090.2980.0280.0770.0110.0790.0000.5341.0000.1490.0700.2530.1490.0740.2070.0700.1200.0580.0890.0470.0290.0910.1870.0280.0280.1000.0270.0830.0530.0310.1700.0390.2250.3100.002
Eyeglasses0.0400.0350.0310.0310.0400.0350.0310.0310.0440.0400.0510.0330.0230.1080.0320.0270.0400.0250.0070.1490.2230.0420.1090.0590.0500.1400.0160.0810.0140.0780.0740.1710.1491.0000.0850.1620.1960.0940.2020.0060.0930.0410.1070.0610.0300.1000.0840.0670.0470.0420.0180.0950.0810.0710.2080.0450.1310.2250.005
Goatee0.0370.0290.0150.0150.0370.0290.0150.0150.0640.0210.0620.0550.0300.0780.0590.0480.0590.0220.1440.1130.1470.0950.1150.0860.0190.1940.0590.1020.0260.0660.1090.1620.0700.0851.0000.0010.2050.1080.3060.0580.4500.0090.5700.0210.0430.0780.0660.0670.5130.0750.0430.1050.1020.0870.2440.0780.0680.1060.026
Gray_Hair0.0400.0260.0230.0240.0400.0260.0230.0240.0390.0270.0410.0280.0270.0240.0430.0300.0520.0160.0440.0990.2020.1720.1510.0620.0880.1960.1170.0520.0070.1030.0510.2110.2530.1620.0011.0000.1450.0000.1840.0090.0350.0130.0110.0590.0100.0590.2540.0390.0060.0090.0100.0850.0550.0360.1610.0410.2440.3640.019
Heavy_Makeup0.0990.0770.0690.0700.0990.0770.0690.0700.2170.1280.2190.1670.0700.1690.1470.1520.1550.1020.2810.4400.4770.2930.1200.1200.1470.2780.0490.2470.1400.0900.1260.1660.1490.1960.2050.1451.0000.2720.6670.1030.1650.0400.3510.2130.0450.2630.1080.3030.1940.1770.0680.3230.3530.1410.8020.2040.2220.2430.012
High_Cheekbones0.0500.0300.0280.0280.0500.0300.0280.0280.4070.1810.3810.3060.0570.2850.5990.2890.5910.1500.1610.1560.1490.0730.0000.0530.0450.0590.0080.1210.0770.0220.0490.0370.0740.0940.1080.0000.2721.0000.2500.4200.0890.0530.1890.2140.0810.0600.0270.2490.1350.6830.0190.1160.2340.0870.2840.1230.0470.0160.018
Male0.1390.1000.0840.0850.1390.1000.0840.0850.2000.1010.2050.1570.0710.1240.1200.1450.1240.0870.4180.4080.3940.3010.1780.1630.1670.3690.1160.3030.0240.1120.2470.2300.2070.2020.3060.1840.6670.2501.0000.1000.2460.0140.5220.1200.0770.2170.1170.2140.2890.1380.0710.3240.3730.1300.7890.2710.3300.2840.020
Mouth_Slightly_Open0.0290.0080.0250.0250.0290.0080.0250.0250.2820.0760.2270.2040.0330.1160.4860.2250.4750.1050.0660.0670.0210.0590.0000.0090.0490.0590.0230.0700.0170.0070.0320.0250.0700.0060.0580.0090.1030.4200.1001.0000.0560.1140.0850.0920.0610.0020.0300.1340.0750.5360.0140.0400.1300.0030.1040.0800.0240.0140.007
Mustache0.0200.0130.0090.0100.0200.0130.0090.0100.0510.0190.0490.0420.0330.0520.0450.0350.0450.0190.0920.0860.1400.1100.0800.0670.0310.2080.0640.0860.0040.0690.1050.1830.1200.0930.4500.0350.1650.0890.2460.0561.0000.0080.4530.0530.0340.0630.0670.0530.3360.0670.0260.0840.0790.0790.1970.0570.1030.1370.015
Narrow_Eyes0.0210.0180.0050.0040.0210.0180.0050.0040.0780.0370.0610.0710.0380.1100.1220.0610.1250.0520.0110.0240.0740.1070.0120.0130.1140.0700.0110.0000.0710.0230.0130.0430.0580.0410.0090.0130.0400.0530.0140.1140.0081.0000.0010.0940.0020.0440.0230.0000.0000.0790.0030.0220.0080.0120.0250.0310.0100.0330.051
No_Beard0.0620.0440.0260.0260.0620.0440.0260.0260.1110.0410.1080.0900.0500.0940.0880.0770.0900.0400.5270.2030.1980.1470.1160.1320.0230.2540.0990.1710.0040.0780.2030.1690.0890.1070.5700.0110.3510.1890.5220.0850.4530.0011.0000.0620.0620.0970.0560.1130.5430.1130.0220.1600.1860.1190.4180.1400.1170.1190.019
Oval_Face0.0890.0750.0820.0820.0890.0750.0820.0820.1650.1310.1770.1200.1420.1010.1750.1050.1740.0820.0850.0130.1940.1360.0110.0000.1120.1060.0320.0500.0830.0460.0180.0190.0470.0610.0210.0590.2130.2140.1200.0920.0530.0940.0621.0000.0360.0120.0060.1190.0490.2060.0040.0380.0750.0460.1570.0570.0520.1130.008
Pale_Skin0.0170.0220.0280.0280.0170.0220.0280.0280.0320.0160.0210.0220.0280.0150.0690.0290.0670.0190.0400.0480.0860.0330.0230.0420.0390.0510.0390.0580.0200.0160.0220.0340.0290.0300.0430.0100.0450.0810.0770.0610.0340.0020.0620.0361.0000.0070.0290.0440.0390.0710.0160.0220.0190.0160.0650.0000.0290.0430.000
Pointy_Nose0.0440.0370.0400.0410.0440.0370.0400.0410.0180.0140.0190.0150.0750.1500.0210.0110.0190.0110.0240.1530.2280.1140.0560.0110.0530.1590.0470.1150.0550.0470.0140.1240.0910.1000.0780.0590.2630.0600.2170.0020.0630.0440.0970.0120.0071.0000.0510.1730.0500.0430.0170.1360.1070.0770.2550.0650.0590.0910.008
Receding_Hairline0.0270.0120.0100.0110.0270.0120.0100.0110.0290.0110.0260.0210.0430.0280.0380.0220.0410.0220.0220.0170.1790.1170.1400.1240.0210.2020.0000.0700.0070.1010.0320.1870.1870.0840.0660.2540.1080.0270.1170.0300.0670.0230.0560.0060.0290.0511.0000.0250.0200.0260.0570.1150.0120.0650.1240.0360.1550.1920.010
Rosy_Cheeks0.0140.0700.0570.0570.0140.0700.0570.0570.1860.0800.1550.1490.0560.1410.2000.1310.2110.0460.0900.2240.1640.0940.0370.0610.0770.0550.0400.1420.0570.0120.0310.0430.0280.0670.0670.0390.3030.2490.2140.1340.0530.0000.1130.1190.0440.1730.0251.0000.0630.2210.0280.1310.2150.0510.2660.1380.0670.0430.009
Sideburns0.0250.0140.0040.0030.0250.0140.0040.0030.0700.0260.0710.0600.0360.0480.0690.0530.0700.0270.2590.1160.1000.1000.0580.0730.0400.1320.0430.0960.0250.0350.1280.1160.0280.0470.5130.0060.1940.1350.2890.0750.3360.0000.5430.0490.0390.0500.0200.0631.0000.0810.0200.0720.1120.0670.2310.0810.0600.0900.021
Smiling0.0630.0220.0430.0420.0630.0220.0430.0420.4520.2100.4390.3450.0570.3290.7140.3190.6980.1660.0680.0940.1480.1130.0090.0520.0130.1010.0010.0900.0530.0230.0020.0360.1000.0420.0750.0090.1770.6830.1380.5360.0670.0790.1130.2060.0710.0430.0260.2210.0811.0000.0050.0750.1700.0640.1820.0890.0000.0330.012
Straight_Hair0.0130.0100.0090.0090.0130.0100.0090.0090.0270.0200.0240.0260.0240.0480.0130.0240.0120.0170.0520.0540.0410.0240.0720.0200.0370.0290.1120.0050.0370.0160.0740.0330.0270.0180.0430.0100.0680.0190.0710.0140.0260.0030.0220.0040.0160.0170.0570.0280.0200.0051.0000.3210.0760.1040.0600.0320.0790.0530.000
Wavy_Hair0.0560.0400.0300.0310.0560.0400.0300.0310.1000.0580.0950.0770.0340.0790.0660.0680.0700.0480.1240.2010.2150.1270.1030.0680.1200.1320.0850.1280.0190.1540.0620.0970.0830.0950.1050.0850.3230.1160.3240.0400.0840.0220.1600.0380.0220.1360.1150.1310.0720.0750.3211.0000.1170.1210.3590.1290.1420.0900.041
Wearing_Earrings0.0500.0910.0810.0820.0500.0910.0810.0820.1490.0730.1420.1180.0310.0560.1510.0990.1550.0460.1620.2950.1240.0970.0610.0580.1260.0580.0010.0950.0580.0000.0760.0560.0530.0810.1020.0550.3530.2340.3730.1300.0790.0080.1860.0750.0190.1070.0120.2150.1120.1700.0760.1171.0000.0510.3670.1930.1290.0300.015
Wearing_Hat0.0240.0190.0130.0140.0240.0190.0130.0140.0640.0450.0670.0510.0300.0900.0580.0490.0610.0370.0340.1000.1390.0050.0300.0800.0130.0650.1040.0830.0160.0980.0200.0590.0310.0710.0870.0360.1410.0870.1300.0030.0790.0120.1190.0460.0160.0770.0650.0510.0670.0640.1040.1210.0511.0000.1600.0390.0290.0360.010
Wearing_Lipstick0.1360.1030.0910.0920.1360.1030.0910.0920.2330.1350.2340.1810.0780.1740.1510.1640.1590.1140.3340.4600.4800.2840.1430.1630.1950.3040.0670.2820.1270.1010.1720.1930.1700.2080.2440.1610.8020.2840.7890.1040.1970.0250.4180.1570.0650.2550.1240.2660.2310.1820.0600.3590.3670.1601.0000.2660.2640.2520.035
Wearing_Necklace0.0620.0720.0630.0640.0620.0720.0630.0640.1060.0650.1090.0810.0480.0480.1000.0680.1010.0470.1160.2200.0690.0530.0500.1140.1500.0360.0420.1440.0040.0040.0710.0490.0390.0450.0780.0410.2040.1230.2710.0800.0570.0310.1400.0570.0000.0650.0360.1380.0810.0890.0320.1290.1930.0390.2661.0000.1040.0140.015
Wearing_Necktie0.0510.0310.0240.0240.0510.0310.0240.0240.0630.0310.0580.0500.0580.0390.0590.0470.0660.0330.0980.1330.1560.1970.1760.0930.0680.2050.0230.1060.0150.0730.0630.1960.2250.1310.0680.2440.2220.0470.3300.0240.1030.0100.1170.0520.0290.0590.1550.0670.0600.0000.0790.1420.1290.0290.2640.1041.0000.2520.001
Young0.0780.0410.0400.0410.0780.0410.0400.0410.0370.0370.0420.0250.0340.0630.0700.0270.0770.0270.0150.1470.3880.2310.1960.0180.1080.2840.1210.0520.0660.1030.0850.2950.3100.2250.1060.3640.2430.0160.2840.0140.1370.0330.1190.1130.0430.0910.1920.0430.0900.0330.0530.0900.0300.0360.2520.0140.2521.0000.026
partition0.0080.0000.0080.0080.0080.0000.0080.0080.0110.0070.0130.0090.0040.0060.0140.0080.0120.0080.0130.0140.0110.0000.0040.0050.0900.0200.0330.0140.0040.0340.0120.0070.0020.0050.0260.0190.0120.0180.0200.0070.0150.0510.0190.0080.0000.0080.0100.0090.0210.0120.0000.0410.0150.0100.0350.0150.0010.0261.000

Missing values

2023-06-17T11:45:48.409794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-17T11:45:49.295352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

image_id5_o_Clock_ShadowArched_EyebrowsAttractiveBags_Under_EyesBaldBangsBig_LipsBig_NoseBlack_HairBlond_HairBlurryBrown_HairBushy_EyebrowsChubbyDouble_ChinEyeglassesGoateeGray_HairHeavy_MakeupHigh_CheekbonesMaleMouth_Slightly_OpenMustacheNarrow_EyesNo_BeardOval_FacePale_SkinPointy_NoseReceding_HairlineRosy_CheeksSideburnsSmilingStraight_HairWavy_HairWearing_EarringsWearing_HatWearing_LipstickWearing_NecklaceWearing_NecktieYoungx_1_xy_1_xwidth_xheight_xx_1_yy_1_ywidth_yheight_ypartitionlefteye_xlefteye_yrighteye_xrighteye_ynose_xnose_yleftmouth_xleftmouth_yrightmouth_xrightmouth_y
0000001.jpg-111-1-1-1-1-1-1-1-11-1-1-1-1-1-111-11-1-11-1-11-1-1-111-11-11-1-11957122631395712263130691091061137714273152108154
1000002.jpg-1-1-11-1-1-11-1-1-11-1-1-1-1-1-1-11-11-1-11-1-1-1-1-1-11-1-1-1-1-1-1-11729422130672942213060691101071128113570151108153
2000003.jpg-1-1-1-1-1-11-1-1-11-1-1-1-1-1-1-1-1-11-1-111-1-11-1-1-1-1-11-1-1-1-1-11216599112621659911260761121041061081287415698158
3000004.jpg-1-11-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-11-1-11-1-1-1-11-11-111-1162225756478162225756478107211310810810113871155101151
4000005.jpg-111-1-1-11-1-1-1-1-1-1-1-1-1-1-11-1-1-1-111-1-11-1-1-1-1-1-1-1-11-1-112361091201662361091201660661141121128611971147104150
5000006.jpg-111-1-1-11-1-1-1-11-1-1-1-1-1-11-1-11-1-11-1-1-1-1-1-1-1-111-11-1-1114667182252146671822520711111061109413174154102153
6000007.jpg1-111-1-1111-1-1-11-1-1-1-1-1-1-11-1-1-11-1-11-1-1-1-11-1-1-1-1-1-11649321129264932112920701121081118513572152104152
7000008.jpg11-11-1-11-11-1-1-1-1-1-1-1-1-1-1-11-1-1-11-1-11-1-1-1-1-1-1-1-1-1-1-1121289218302212892183020711101061118413773155104153
8000009.jpg-111-1-111-1-1-1-1-1-1-1-1-1-1-111-11-1-111-11-11-11-1-11-11-1-116002743434756002743434750681131101119713966152109150
9000010.jpg-1-11-1-1-1-1-1-1-1-1-1-1-1-1-1-1-111-1-1-1-11-1-1-1-1-1-1-1-11-1-11-1-111131102112921131102112920681111081128913670151107151
image_id5_o_Clock_ShadowArched_EyebrowsAttractiveBags_Under_EyesBaldBangsBig_LipsBig_NoseBlack_HairBlond_HairBlurryBrown_HairBushy_EyebrowsChubbyDouble_ChinEyeglassesGoateeGray_HairHeavy_MakeupHigh_CheekbonesMaleMouth_Slightly_OpenMustacheNarrow_EyesNo_BeardOval_FacePale_SkinPointy_NoseReceding_HairlineRosy_CheeksSideburnsSmilingStraight_HairWavy_HairWearing_EarringsWearing_HatWearing_LipstickWearing_NecklaceWearing_NecktieYoungx_1_xy_1_xwidth_xheight_xx_1_yy_1_ywidth_yheight_ypartitionlefteye_xlefteye_yrighteye_xrighteye_ynose_xnose_yleftmouth_xleftmouth_yrightmouth_xrightmouth_y
202589202590.jpg-1-1-1-1-1-1-11-1-1-1-1-1-1-11-11-111-1-1-11-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1359323732835932373282691111081128913473152104152
202590202591.jpg-111-1-1-1111-1-1-1-1-1-1-1-1-111-11-111-1-1-111-111-11-111-1170045210451447700452104514472691111081129513567152110151
202591202592.jpg-11-1-1-1-1-1-1-11-1-1-1-1-1-1-1-111-11-1-11-1-1-1-1-1-11-11-1-11-1-114091081071484091081071482671111101138513467149112151
202592202593.jpg-1-11-1-11-1-1-1-1-1-1-1-1-1-1-1-111-11-1111-11-1-1-11-1-1-1-11-1-1192103190263921031902632681121091128614169151107152
202593202594.jpg-111-1-1-11-1-1-1-1-11-1-1-1-1-11-1-1-1-1-11-1-11-1-1-1-1-111-11-1-111081272443381081272443382691111081119113974153103151
202594202595.jpg-1-11-1-1-11-1-11-1-1-1-1-1-1-1-1-1-1-1-1-1-11-1-1-1-1-1-1-1-1-1-1-11-1-111381912213061381912213062691111081118914073151104153
202595202596.jpg-1-1-1-1-111-1-11-1-1-1-1-1-1-1-1-1111-111-11-1-1-1-111-1-1-1-1-1-111371291141581371291141582671121101128514166150110150
202596202597.jpg-1-1-1-1-1-1-1-11-1-1-1-1-1-11-1-1-1111-1-11-1-1-1-1-1-11-1-1-1-1-1-1-115376911265376911262691111071119213768151109153
202597202598.jpg-111-1-1-11-11-1-1-11-1-1-1-1-111-1-1-1-111-11-11-11-111-11-1-11195289112619528911262691111081119013771153106151
202598202599.jpg-111-1-1-1-1-1-11-1-1-1-1-1-1-1-11-1-1-1-1-11-111-1-1-1-1-11-1-11-1-111011011792481011011792482681111091128113675150103152